Add https://www.reddit.com/r/Ultralytics/ badge (#13284)

* Refactor code for speed and clarity

* Update README.zh-CN.md

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

README.md

@@ -10,7 +10,7 @@
     <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
     <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
-    <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
+    <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
     <br>
     <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
     <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>

README.zh-CN.md

@@ -10,6 +10,7 @@
     <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
     <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+    <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
     <br>
     <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
     <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>

export.py

@@ -91,6 +91,8 @@ MACOS = platform.system() == "Darwin"  # macOS environment
 
 
 class iOSModel(torch.nn.Module):
+    """An iOS-compatible wrapper for YOLOv5 models that normalizes input images based on their dimensions."""
+
     def __init__(self, model, im):
         """
         Initializes an iOS compatible model with normalization based on image dimensions.

models/common.py

@@ -71,7 +71,8 @@ def autopad(k, p=None, d=1):
 
 
 class Conv(nn.Module):
-    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
+    """Applies a convolution, batch normalization, and activation function to an input tensor in a neural network."""
+
     default_act = nn.SiLU()  # default activation
 
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
@@ -91,7 +92,8 @@ class Conv(nn.Module):
 
 
 class DWConv(Conv):
-    # Depth-wise convolution
+    """Implements a depth-wise convolution layer with optional activation for efficient spatial filtering."""
+
     def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
         """Initializes a depth-wise convolution layer with optional activation; args: input channels (c1), output
         channels (c2), kernel size (k), stride (s), dilation (d), and activation flag (act).
@@ -100,7 +102,8 @@ class DWConv(Conv):
 
 
 class DWConvTranspose2d(nn.ConvTranspose2d):
-    # Depth-wise transpose convolution
+    """A depth-wise transpose convolutional layer for upsampling in neural networks, particularly in YOLOv5 models."""
+
     def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):
         """Initializes a depth-wise transpose convolutional layer for YOLOv5; args: input channels (c1), output channels
         (c2), kernel size (k), stride (s), input padding (p1), output padding (p2).
@@ -109,7 +112,8 @@ class DWConvTranspose2d(nn.ConvTranspose2d):
 
 
 class TransformerLayer(nn.Module):
-    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+    """Transformer layer with multihead attention and linear layers, optimized by removing LayerNorm."""
+
     def __init__(self, c, num_heads):
         """
         Initializes a transformer layer, sans LayerNorm for performance, with multihead attention and linear layers.
@@ -132,7 +136,8 @@ class TransformerLayer(nn.Module):
 
 
 class TransformerBlock(nn.Module):
-    # Vision Transformer https://arxiv.org/abs/2010.11929
+    """A Transformer block for vision tasks with convolution, position embeddings, and Transformer layers."""
+
     def __init__(self, c1, c2, num_heads, num_layers):
         """Initializes a Transformer block for vision tasks, adapting dimensions if necessary and stacking specified
         layers.
@@ -157,7 +162,8 @@ class TransformerBlock(nn.Module):
 
 
 class Bottleneck(nn.Module):
-    # Standard bottleneck
+    """A bottleneck layer with optional shortcut and group convolution for efficient feature extraction."""
+
     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
         """Initializes a standard bottleneck layer with optional shortcut and group convolution, supporting channel
         expansion.
@@ -176,7 +182,8 @@ class Bottleneck(nn.Module):
 
 
 class BottleneckCSP(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    """CSP bottleneck layer for feature extraction with cross-stage partial connections and optional shortcuts."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes CSP bottleneck with optional shortcuts; args: ch_in, ch_out, number of repeats, shortcut bool,
         groups, expansion.
@@ -201,7 +208,8 @@ class BottleneckCSP(nn.Module):
 
 
 class CrossConv(nn.Module):
-    # Cross Convolution Downsample
+    """Implements a cross convolution layer with downsampling, expansion, and optional shortcut."""
+
     def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
         """
         Initializes CrossConv with downsampling, expanding, and optionally shortcutting; `c1` input, `c2` output
@@ -221,7 +229,8 @@ class CrossConv(nn.Module):
 
 
 class C3(nn.Module):
-    # CSP Bottleneck with 3 convolutions
+    """Implements a CSP Bottleneck module with three convolutions for enhanced feature extraction in neural networks."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, group
         convolutions, and expansion.
@@ -239,7 +248,8 @@ class C3(nn.Module):
 
 
 class C3x(C3):
-    # C3 module with cross-convolutions
+    """Extends the C3 module with cross-convolutions for enhanced feature extraction in neural networks."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes C3x module with cross-convolutions, extending C3 with customizable channel dimensions, groups,
         and expansion.
@@ -250,7 +260,8 @@ class C3x(C3):
 
 
 class C3TR(C3):
-    # C3 module with TransformerBlock()
+    """C3 module with TransformerBlock for enhanced feature extraction in object detection models."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes C3 module with TransformerBlock for enhanced feature extraction, accepts channel sizes, shortcut
         config, group, and expansion.
@@ -261,7 +272,8 @@ class C3TR(C3):
 
 
 class C3SPP(C3):
-    # C3 module with SPP()
+    """Extends the C3 module with an SPP layer for enhanced spatial feature extraction and customizable channels."""
+
     def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
         """Initializes a C3 module with SPP layer for advanced spatial feature extraction, given channel sizes, kernel
         sizes, shortcut, group, and expansion ratio.
@@ -272,7 +284,8 @@ class C3SPP(C3):
 
 
 class C3Ghost(C3):
-    # C3 module with GhostBottleneck()
+    """Implements a C3 module with Ghost Bottlenecks for efficient feature extraction in YOLOv5."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes YOLOv5's C3 module with Ghost Bottlenecks for efficient feature extraction."""
         super().__init__(c1, c2, n, shortcut, g, e)
@@ -281,7 +294,8 @@ class C3Ghost(C3):
 
 
 class SPP(nn.Module):
-    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
+    """Implements Spatial Pyramid Pooling (SPP) for feature extraction, ref: https://arxiv.org/abs/1406.4729."""
+
     def __init__(self, c1, c2, k=(5, 9, 13)):
         """Initializes SPP layer with Spatial Pyramid Pooling, ref: https://arxiv.org/abs/1406.4729, args: c1 (input channels), c2 (output channels), k (kernel sizes)."""
         super().__init__()
@@ -301,7 +315,8 @@ class SPP(nn.Module):
 
 
 class SPPF(nn.Module):
-    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+    """Implements a fast Spatial Pyramid Pooling (SPPF) layer for efficient feature extraction in YOLOv5 models."""
+
     def __init__(self, c1, c2, k=5):
         """
         Initializes YOLOv5 SPPF layer with given channels and kernel size for YOLOv5 model, combining convolution and
@@ -326,7 +341,8 @@ class SPPF(nn.Module):
 
 
 class Focus(nn.Module):
-    # Focus wh information into c-space
+    """Focuses spatial information into channel space using slicing and convolution for efficient feature extraction."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
         """Initializes Focus module to concentrate width-height info into channel space with configurable convolution
         parameters.
@@ -342,7 +358,8 @@ class Focus(nn.Module):
 
 
 class GhostConv(nn.Module):
-    # Ghost Convolution https://github.com/huawei-noah/ghostnet
+    """Implements Ghost Convolution for efficient feature extraction, see https://github.com/huawei-noah/ghostnet."""
+
     def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
         """Initializes GhostConv with in/out channels, kernel size, stride, groups, and activation; halves out channels
         for efficiency.
@@ -359,7 +376,8 @@ class GhostConv(nn.Module):
 
 
 class GhostBottleneck(nn.Module):
-    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+    """Efficient bottleneck layer using Ghost Convolutions, see https://github.com/huawei-noah/ghostnet."""
+
     def __init__(self, c1, c2, k=3, s=1):
         """Initializes GhostBottleneck with ch_in `c1`, ch_out `c2`, kernel size `k`, stride `s`; see https://github.com/huawei-noah/ghostnet."""
         super().__init__()
@@ -379,7 +397,8 @@ class GhostBottleneck(nn.Module):
 
 
 class Contract(nn.Module):
-    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+    """Contracts spatial dimensions into channel dimensions for efficient processing in neural networks."""
+
     def __init__(self, gain=2):
         """Initializes a layer to contract spatial dimensions (width-height) into channels, e.g., input shape
         (1,64,80,80) to (1,256,40,40).
@@ -399,7 +418,8 @@ class Contract(nn.Module):
 
 
 class Expand(nn.Module):
-    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+    """Expands spatial dimensions by redistributing channels, e.g., from (1,64,80,80) to (1,16,160,160)."""
+
     def __init__(self, gain=2):
         """
         Initializes the Expand module to increase spatial dimensions by redistributing channels, with an optional gain
@@ -422,7 +442,8 @@ class Expand(nn.Module):
 
 
 class Concat(nn.Module):
-    # Concatenate a list of tensors along dimension
+    """Concatenates tensors along a specified dimension for efficient tensor manipulation in neural networks."""
+
     def __init__(self, dimension=1):
         """Initializes a Concat module to concatenate tensors along a specified dimension."""
         super().__init__()
@@ -436,7 +457,8 @@ class Concat(nn.Module):
 
 
 class DetectMultiBackend(nn.Module):
-    # YOLOv5 MultiBackend class for python inference on various backends
+    """YOLOv5 MultiBackend class for inference on various backends including PyTorch, ONNX, TensorRT, and more."""
+
     def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True):
         """Initializes DetectMultiBackend with support for various inference backends, including PyTorch and ONNX."""
         #   PyTorch:              weights = *.pt
@@ -778,7 +800,8 @@ class DetectMultiBackend(nn.Module):
 
 
 class AutoShape(nn.Module):
-    # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    """AutoShape class for robust YOLOv5 inference with preprocessing, NMS, and support for various input formats."""
+
     conf = 0.25  # NMS confidence threshold
     iou = 0.45  # NMS IoU threshold
     agnostic = False  # NMS class-agnostic
@@ -889,7 +912,8 @@ class AutoShape(nn.Module):
 
 
 class Detections:
-    # YOLOv5 detections class for inference results
+    """Manages YOLOv5 detection results with methods for visualization, saving, cropping, and exporting detections."""
+
     def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
         """Initializes the YOLOv5 Detections class with image info, predictions, filenames, timing and normalization."""
         super().__init__()
@@ -1047,7 +1071,8 @@ class Detections:
 
 
 class Proto(nn.Module):
-    # YOLOv5 mask Proto module for segmentation models
+    """YOLOv5 mask Proto module for segmentation models, performing convolutions and upsampling on input tensors."""
+
     def __init__(self, c1, c_=256, c2=32):
         """Initializes YOLOv5 Proto module for segmentation with input, proto, and mask channels configuration."""
         super().__init__()
@@ -1062,7 +1087,8 @@ class Proto(nn.Module):
 
 
 class Classify(nn.Module):
-    # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    """YOLOv5 classification head with convolution, pooling, and dropout layers for channel transformation."""
+
     def __init__(
         self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0
     ):  # ch_in, ch_out, kernel, stride, padding, groups, dropout probability

models/tf.py

@@ -49,7 +49,8 @@ from utils.general import LOGGER, make_divisible, print_args
 
 
 class TFBN(keras.layers.Layer):
-    # TensorFlow BatchNormalization wrapper
+    """TensorFlow BatchNormalization wrapper for initializing with optional pretrained weights."""
+
     def __init__(self, w=None):
         """Initializes a TensorFlow BatchNormalization layer with optional pretrained weights."""
         super().__init__()
@@ -67,7 +68,8 @@ class TFBN(keras.layers.Layer):
 
 
 class TFPad(keras.layers.Layer):
-    # Pad inputs in spatial dimensions 1 and 2
+    """Pads input tensors in spatial dimensions 1 and 2 with specified integer or tuple padding values."""
+
     def __init__(self, pad):
         """
         Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple
@@ -87,7 +89,8 @@ class TFPad(keras.layers.Layer):
 
 
 class TFConv(keras.layers.Layer):
-    # Standard convolution
+    """Implements a standard convolutional layer with optional batch normalization and activation for TensorFlow."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
         """
         Initializes a standard convolution layer with optional batch normalization and activation; supports only
@@ -118,7 +121,8 @@ class TFConv(keras.layers.Layer):
 
 
 class TFDWConv(keras.layers.Layer):
-    # Depthwise convolution
+    """Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
         """
         Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow
@@ -147,7 +151,8 @@ class TFDWConv(keras.layers.Layer):
 
 
 class TFDWConvTranspose2d(keras.layers.Layer):
-    # Depthwise ConvTranspose2d
+    """Implements a depthwise ConvTranspose2D layer for TensorFlow with specific settings."""
+
     def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
         """
         Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings.
@@ -179,7 +184,8 @@ class TFDWConvTranspose2d(keras.layers.Layer):
 
 
 class TFFocus(keras.layers.Layer):
-    # Focus wh information into c-space
+    """Focuses spatial information into channel space using pixel shuffling and convolution for TensorFlow models."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
         """
         Initializes TFFocus layer to focus width and height information into channel space with custom convolution
@@ -201,7 +207,8 @@ class TFFocus(keras.layers.Layer):
 
 
 class TFBottleneck(keras.layers.Layer):
-    # Standard bottleneck
+    """Implements a TensorFlow bottleneck layer with optional shortcut connections for efficient feature extraction."""
+
     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):
         """
         Initializes a standard bottleneck layer for TensorFlow models, expanding and contracting channels with optional
@@ -223,7 +230,8 @@ class TFBottleneck(keras.layers.Layer):
 
 
 class TFCrossConv(keras.layers.Layer):
-    # Cross Convolution
+    """Implements a cross convolutional layer with optional expansion, grouping, and shortcut for TensorFlow."""
+
     def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
         """Initializes cross convolution layer with optional expansion, grouping, and shortcut addition capabilities."""
         super().__init__()
@@ -238,7 +246,8 @@ class TFCrossConv(keras.layers.Layer):
 
 
 class TFConv2d(keras.layers.Layer):
-    # Substitution for PyTorch nn.Conv2D
+    """Implements a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D for specified filters and stride."""
+
     def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
         """Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter
         sizes and stride.
@@ -261,7 +270,8 @@ class TFConv2d(keras.layers.Layer):
 
 
 class TFBottleneckCSP(keras.layers.Layer):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    """Implements a CSP bottleneck layer for TensorFlow models to enhance gradient flow and efficiency."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
         """
         Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion
@@ -289,7 +299,8 @@ class TFBottleneckCSP(keras.layers.Layer):
 
 
 class TFC3(keras.layers.Layer):
-    # CSP Bottleneck with 3 convolutions
+    """CSP bottleneck layer with 3 convolutions for TensorFlow, supporting optional shortcuts and group convolutions."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
         """
         Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions.
@@ -313,7 +324,8 @@ class TFC3(keras.layers.Layer):
 
 
 class TFC3x(keras.layers.Layer):
-    # 3 module with cross-convolutions
+    """A TensorFlow layer for enhanced feature extraction using cross-convolutions in object detection models."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
         """
         Initializes layer with cross-convolutions for enhanced feature extraction in object detection models.
@@ -335,7 +347,8 @@ class TFC3x(keras.layers.Layer):
 
 
 class TFSPP(keras.layers.Layer):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
+    """Implements spatial pyramid pooling for YOLOv3-SPP with specific channels and kernel sizes."""
+
     def __init__(self, c1, c2, k=(5, 9, 13), w=None):
         """Initializes a YOLOv3-SPP layer with specific input/output channels and kernel sizes for pooling."""
         super().__init__()
@@ -351,7 +364,8 @@ class TFSPP(keras.layers.Layer):
 
 
 class TFSPPF(keras.layers.Layer):
-    # Spatial pyramid pooling-Fast layer
+    """Implements a fast spatial pyramid pooling layer for TensorFlow with optimized feature extraction."""
+
     def __init__(self, c1, c2, k=5, w=None):
         """Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and
         weights.
@@ -373,7 +387,8 @@ class TFSPPF(keras.layers.Layer):
 
 
 class TFDetect(keras.layers.Layer):
-    # TF YOLOv5 Detect layer
+    """Implements YOLOv5 object detection layer in TensorFlow for predicting bounding boxes and class probabilities."""
+
     def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):
         """Initializes YOLOv5 detection layer for TensorFlow with configurable classes, anchors, channels, and image
         size.
@@ -427,7 +442,8 @@ class TFDetect(keras.layers.Layer):
 
 
 class TFSegment(TFDetect):
-    # YOLOv5 Segment head for segmentation models
+    """YOLOv5 segmentation head for TensorFlow, combining detection and segmentation."""
+
     def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
         """Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation
         models.
@@ -450,6 +466,8 @@ class TFSegment(TFDetect):
 
 
 class TFProto(keras.layers.Layer):
+    """Implements convolutional and upsampling layers for feature extraction in YOLOv5 segmentation."""
+
     def __init__(self, c1, c_=256, c2=32, w=None):
         """Initializes TFProto layer with convolutional and upsampling layers for feature extraction and
         transformation.
@@ -466,7 +484,8 @@ class TFProto(keras.layers.Layer):
 
 
 class TFUpsample(keras.layers.Layer):
-    # TF version of torch.nn.Upsample()
+    """Implements a TensorFlow upsampling layer with specified size, scale factor, and interpolation mode."""
+
     def __init__(self, size, scale_factor, mode, w=None):
         """
         Initializes a TensorFlow upsampling layer with specified size, scale_factor, and mode, ensuring scale_factor is
@@ -488,7 +507,8 @@ class TFUpsample(keras.layers.Layer):
 
 
 class TFConcat(keras.layers.Layer):
-    # TF version of torch.concat()
+    """Implements TensorFlow's version of torch.concat() for concatenating tensors along the last dimension."""
+
     def __init__(self, dimension=1, w=None):
         """Initializes a TensorFlow layer for NCHW to NHWC concatenation, requiring dimension=1."""
         super().__init__()
@@ -581,7 +601,8 @@ def parse_model(d, ch, model, imgsz):
 
 
 class TFModel:
-    # TF YOLOv5 model
+    """Implements YOLOv5 model in TensorFlow, supporting TensorFlow, Keras, and TFLite formats for object detection."""
+
     def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)):
         """Initializes TF YOLOv5 model with specified configuration, channels, classes, model instance, and input
         size.
@@ -653,7 +674,8 @@ class TFModel:
 
 
 class AgnosticNMS(keras.layers.Layer):
-    # TF Agnostic NMS
+    """Performs agnostic non-maximum suppression (NMS) on detected objects using IoU and confidence thresholds."""
+
     def call(self, input, topk_all, iou_thres, conf_thres):
         """Performs agnostic NMS on input tensors using given thresholds and top-K selection."""
         return tf.map_fn(

models/yolo.py

@@ -70,7 +70,8 @@ except ImportError:
 
 
 class Detect(nn.Module):
-    # YOLOv5 Detect head for detection models
+    """YOLOv5 Detect head for processing input tensors and generating detection outputs in object detection models."""
+
     stride = None  # strides computed during build
     dynamic = False  # force grid reconstruction
     export = False  # export mode
@@ -127,7 +128,8 @@ class Detect(nn.Module):
 
 
 class Segment(Detect):
-    # YOLOv5 Segment head for segmentation models
+    """YOLOv5 Segment head for segmentation models, extending Detect with mask and prototype layers."""
+
     def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
         """Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
         super().__init__(nc, anchors, ch, inplace)
@@ -214,7 +216,8 @@ class BaseModel(nn.Module):
 
 
 class DetectionModel(BaseModel):
-    # YOLOv5 detection model
+    """YOLOv5 detection model class for object detection tasks, supporting custom configurations and anchors."""
+
     def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):
         """Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
         super().__init__()
@@ -332,14 +335,16 @@ Model = DetectionModel  # retain YOLOv5 'Model' class for backwards compatibilit
 
 
 class SegmentationModel(DetectionModel):
-    # YOLOv5 segmentation model
+    """YOLOv5 segmentation model for object detection and segmentation tasks with configurable parameters."""
+
     def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
         """Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list)."""
         super().__init__(cfg, ch, nc, anchors)
 
 
 class ClassificationModel(BaseModel):
-    # YOLOv5 classification model
+    """YOLOv5 classification model for image classification tasks, initialized with a config file or detection model."""
+
     def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
         """Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
         index.

utils/__init__.py

@@ -12,7 +12,8 @@ def emojis(str=""):
 
 
 class TryExcept(contextlib.ContextDecorator):
-    # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
+    """A context manager and decorator for error handling that prints an optional message with emojis on exception."""
+
     def __init__(self, msg=""):
         """Initializes TryExcept with an optional message, used as a decorator or context manager for error handling."""
         self.msg = msg

utils/activations.py

@@ -7,6 +7,8 @@ import torch.nn.functional as F
 
 
 class SiLU(nn.Module):
+    """Applies the Sigmoid-weighted Linear Unit (SiLU) activation function, also known as Swish."""
+
     @staticmethod
     def forward(x):
         """
@@ -18,6 +20,8 @@ class SiLU(nn.Module):
 
 
 class Hardswish(nn.Module):
+    """Applies the Hardswish activation function, which is efficient for mobile and embedded devices."""
+
     @staticmethod
     def forward(x):
         """
@@ -38,7 +42,11 @@ class Mish(nn.Module):
 
 
 class MemoryEfficientMish(nn.Module):
+    """Efficiently applies the Mish activation function using custom autograd for reduced memory usage."""
+
     class F(torch.autograd.Function):
+        """Implements a custom autograd function for memory-efficient Mish activation."""
+
         @staticmethod
         def forward(ctx, x):
             """Applies the Mish activation function, a smooth ReLU alternative, to the input tensor `x`."""

utils/augmentations.py

@@ -18,7 +18,8 @@ IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation
 
 
 class Albumentations:
-    # YOLOv5 Albumentations class (optional, only used if package is installed)
+    """Provides optional data augmentation for YOLOv5 using Albumentations library if installed."""
+
     def __init__(self, size=640):
         """Initializes Albumentations class for optional data augmentation in YOLOv5 with specified input size."""
         self.transform = None
@@ -378,7 +379,8 @@ def classify_transforms(size=224):
 
 
 class LetterBox:
-    # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+    """Resizes and pads images to specified dimensions while maintaining aspect ratio for YOLOv5 preprocessing."""
+
     def __init__(self, size=(640, 640), auto=False, stride=32):
         """Initializes a LetterBox object for YOLOv5 image preprocessing with optional auto sizing and stride
         adjustment.
@@ -405,7 +407,8 @@ class LetterBox:
 
 
 class CenterCrop:
-    # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
+    """Applies center crop to an image, resizing it to the specified size while maintaining aspect ratio."""
+
     def __init__(self, size=640):
         """Initializes CenterCrop for image preprocessing, accepting single int or tuple for size, defaults to 640."""
         super().__init__()
@@ -424,7 +427,8 @@ class CenterCrop:
 
 
 class ToTensor:
-    # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+    """Converts BGR np.array image from HWC to RGB CHW format, normalizes to [0, 1], and supports FP16 if half=True."""
+
     def __init__(self, half=False):
         """Initializes ToTensor for YOLOv5 image preprocessing, with optional half precision (half=True for FP16)."""
         super().__init__()

utils/dataloaders.py

@@ -131,6 +131,8 @@ def seed_worker(worker_id):
 # Inherit from DistributedSampler and override iterator
 # https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
 class SmartDistributedSampler(distributed.DistributedSampler):
+    """A distributed sampler ensuring deterministic shuffling and balanced data distribution across GPUs."""
+
     def __iter__(self):
         """Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed."""
         g = torch.Generator()
@@ -259,7 +261,8 @@ class _RepeatSampler:
 
 
 class LoadScreenshots:
-    # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
+    """Loads and processes screenshots for YOLOv5 detection from specified screen regions using mss."""
+
     def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
         """
         Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and
@@ -428,7 +431,8 @@ class LoadImages:
 
 
 class LoadStreams:
-    # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`
+    """Loads and processes video streams for YOLOv5, supporting various sources including YouTube and IP cameras."""
+
     def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
         """Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including
         YouTube.
@@ -531,7 +535,8 @@ def img2label_paths(img_paths):
 
 
 class LoadImagesAndLabels(Dataset):
-    # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
+    """Loads images and their corresponding labels for training and validation in YOLOv5."""
+
     cache_version = 0.6  # dataset labels *.cache version
     rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
 

utils/general.py

@@ -188,7 +188,8 @@ CONFIG_DIR = user_config_dir()  # Ultralytics settings dir
 
 
 class Profile(contextlib.ContextDecorator):
-    # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
+    """Context manager and decorator for profiling code execution time, with optional CUDA synchronization."""
+
     def __init__(self, t=0.0, device: torch.device = None):
         """Initializes a profiling context for YOLOv5 with optional timing threshold and device specification."""
         self.t = t
@@ -213,7 +214,8 @@ class Profile(contextlib.ContextDecorator):
 
 
 class Timeout(contextlib.ContextDecorator):
-    # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
+    """Enforces a timeout on code execution, raising TimeoutError if the specified duration is exceeded."""
+
     def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True):
         """Initializes a timeout context/decorator with defined seconds, optional message, and error suppression."""
         self.seconds = int(seconds)
@@ -239,7 +241,8 @@ class Timeout(contextlib.ContextDecorator):
 
 
 class WorkingDirectory(contextlib.ContextDecorator):
-    # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
+    """Context manager/decorator to temporarily change the working directory within a 'with' statement or decorator."""
+
     def __init__(self, new_dir):
         """Initializes a context manager/decorator to temporarily change the working directory."""
         self.dir = new_dir  # new dir

utils/loggers/__init__.py

@@ -76,7 +76,8 @@ def _json_default(value):
 
 
 class Loggers:
-    # YOLOv5 Loggers class
+    """Initializes and manages various logging utilities for tracking YOLOv5 training and validation metrics."""
+
     def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
         """Initializes loggers for YOLOv5 training and validation metrics, paths, and options."""
         self.save_dir = save_dir

utils/loss.py

@@ -14,7 +14,8 @@ def smooth_BCE(eps=0.1):
 
 
 class BCEBlurWithLogitsLoss(nn.Module):
-    # BCEwithLogitLoss() with reduced missing label effects.
+    """Modified BCEWithLogitsLoss to reduce missing label effects in YOLOv5 training with optional alpha smoothing."""
+
     def __init__(self, alpha=0.05):
         """Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing
         parameter.
@@ -37,7 +38,8 @@ class BCEBlurWithLogitsLoss(nn.Module):
 
 
 class FocalLoss(nn.Module):
-    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+    """Applies focal loss to address class imbalance by modifying BCEWithLogitsLoss with gamma and alpha parameters."""
+
     def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
         """Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to
         'none'.
@@ -71,7 +73,8 @@ class FocalLoss(nn.Module):
 
 
 class QFocalLoss(nn.Module):
-    # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+    """Implements Quality Focal Loss to address class imbalance by modulating loss based on prediction confidence."""
+
     def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
         """Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'."""
         super().__init__()
@@ -101,6 +104,8 @@ class QFocalLoss(nn.Module):
 
 
 class ComputeLoss:
+    """Computes the total loss for YOLOv5 model predictions, including classification, box, and objectness losses."""
+
     sort_obj_iou = False
 
     # Compute losses

utils/metrics.py

@@ -122,7 +122,8 @@ def compute_ap(recall, precision):
 
 
 class ConfusionMatrix:
-    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
+    """Generates and visualizes a confusion matrix for evaluating object detection classification performance."""
+
     def __init__(self, nc, conf=0.25, iou_thres=0.45):
         """Initializes ConfusionMatrix with given number of classes, confidence, and IoU threshold."""
         self.matrix = np.zeros((nc + 1, nc + 1))

utils/plots.py

@@ -29,7 +29,8 @@ matplotlib.use("Agg")  # for writing to files only
 
 
 class Colors:
-    # Ultralytics color palette https://ultralytics.com/
+    """Provides an RGB color palette derived from Ultralytics color scheme for visualization tasks."""
+
     def __init__(self):
         """
         Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB.

utils/segment/dataloaders.py

@@ -83,6 +83,8 @@ def create_dataloader(
 
 
 class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
+    """Loads images, labels, and segmentation masks for training and testing YOLO models with augmentation support."""
+
     def __init__(
         self,
         path,

utils/segment/loss.py

@@ -12,7 +12,8 @@ from .general import crop_mask
 
 
 class ComputeLoss:
-    # Compute losses
+    """Computes the YOLOv5 model's loss components including classification, objectness, box, and mask losses."""
+
     def __init__(self, model, autobalance=False, overlap=False):
         """Initializes the compute loss function for YOLOv5 models with options for autobalancing and overlap
         handling.

utils/segment/metrics.py

@@ -54,6 +54,8 @@ def ap_per_class_box_and_mask(
 
 
 class Metric:
+    """Computes performance metrics like precision, recall, F1 score, and average precision for model evaluation."""
+
     def __init__(self) -> None:
         """Initializes performance metric attributes for precision, recall, F1 score, average precision, and class
         indices.

utils/torch_utils.py

@@ -420,7 +420,8 @@ def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, re
 
 
 class EarlyStopping:
-    # YOLOv5 simple early stopper
+    """Implements early stopping to halt training when no improvement is observed for a specified number of epochs."""
+
     def __init__(self, patience=30):
         """Initializes simple early stopping mechanism for YOLOv5, with adjustable patience for non-improving epochs."""
         self.best_fitness = 0.0  # i.e. mAP